Communications designer Lauren Manning has taken the data of her personal food consumption for the last two years and visualized it in 40 different ways. The visualizations vary from extracting specific data points in a bar graph (e.g. french fries consumed per month), to all-inclusive data profiles depicting her consumption trends over the course of two years. As a method of presentation, the layouts facilitate comparing habits from one year to the next. For example, it appears Lauren has cut back on her pork consumption in 2010.

The matrix above was featured as part of her thesis exhibition to guide onlookers along the x-axis from visualizations simple to complex, and on the y-axis from literal to abstract. She also included ‘experience cards’ to rate each visualization for its effect.

Her explanation of the project is below:

“It’s like comparing apples to oranges.” This phrase is the best way to describe the current state of data visualizations. For the designer, its easy to find good visualizations and bad ones, but how to apply the successful elements of particular designs to one’s own data set starts to get a little more complicated. Data sets vary tremendously, so one man’s brilliant solution can be another’s complete failure. Instead of seeing many excellent visualizations of all different data sets, what if you could see tons of visualizations of the same data set? What new comparisons, knowledge and structure might be developed from this?